166 lines
6.6 KiB
Markdown
166 lines
6.6 KiB
Markdown
# TTS ONNX Inference Examples
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This guide provides examples for running TTS inference using `example_onnx.go`.
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## 📰 Update News
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**2026.01.06** - 🎉 **Supertonic 2** released with multilingual support! Now supports English (`en`), Korean (`ko`), Spanish (`es`), Portuguese (`pt`), and French (`fr`). [Demo](https://huggingface.co/spaces/Supertone/supertonic-2) | [Models](https://huggingface.co/Supertone/supertonic-2)
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**2025.12.10** - Added [6 new voice styles](https://huggingface.co/Supertone/supertonic/tree/b10dbaf18b316159be75b34d24f740008fddd381) (M3, M4, M5, F3, F4, F5). See [Voices](https://supertone-inc.github.io/supertonic-py/voices/) for details
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**2025.12.08** - Optimized ONNX models via [OnnxSlim](https://github.com/inisis/OnnxSlim) now available on [Hugging Face Models](https://huggingface.co/Supertone/supertonic)
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**2025.11.23** - Enhanced text preprocessing with comprehensive normalization, emoji removal, symbol replacement, and punctuation handling for improved synthesis quality.
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**2025.11.19** - Added `--speed` parameter to control speech synthesis speed (default: 1.05, recommended range: 0.9-1.5).
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**2025.11.19** - Added automatic text chunking for long-form inference. Long texts are split into chunks and synthesized with natural pauses.
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## Installation
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This project uses Go modules for dependency management.
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### Prerequisites
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1. Install Go 1.21 or later from [https://golang.org/dl/](https://golang.org/dl/)
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2. Install ONNX Runtime C library:
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**macOS (via Homebrew):**
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```bash
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brew install onnxruntime
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```
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**Linux:**
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```bash
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# Download ONNX Runtime from GitHub releases
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wget https://github.com/microsoft/onnxruntime/releases/download/v1.16.0/onnxruntime-linux-x64-1.16.0.tgz
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tar -xzf onnxruntime-linux-x64-1.16.0.tgz
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sudo cp onnxruntime-linux-x64-1.16.0/lib/* /usr/local/lib/
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sudo cp -r onnxruntime-linux-x64-1.16.0/include/* /usr/local/include/
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sudo ldconfig
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```
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### Install Go dependencies
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```bash
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go mod download
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```
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### Configure ONNX Runtime Library Path (Optional)
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If the ONNX Runtime library is not in a standard location, set the environment variable:
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**Automatic Detection (Recommended):**
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```bash
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# macOS
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export ONNXRUNTIME_LIB_PATH=$(brew --prefix onnxruntime 2>/dev/null)/lib/libonnxruntime.dylib
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# Linux
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export ONNXRUNTIME_LIB_PATH=$(find /usr/local/lib /usr/lib -name "libonnxruntime.so*" 2>/dev/null | head -n 1)
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```
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**Manual Configuration:**
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```bash
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export ONNXRUNTIME_LIB_PATH=/path/to/libonnxruntime.so # Linux
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# or
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export ONNXRUNTIME_LIB_PATH=/path/to/libonnxruntime.dylib # macOS
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```
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## Basic Usage
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### Example 1: Default Inference
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Run inference with default settings:
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```bash
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go run example_onnx.go helper.go
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```
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This will use:
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- Voice style: `assets/voice_styles/M1.json`
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- Text: "This morning, I took a walk in the park, and the sound of the birds and the breeze was so pleasant that I stopped for a long time just to listen."
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- Output directory: `results/`
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- Total steps: 5
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- Number of generations: 4
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### Example 2: Batch Inference
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Process multiple voice styles and texts at once:
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```bash
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go run example_onnx.go helper.go \
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--batch \
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-voice-style "assets/voice_styles/M1.json,assets/voice_styles/F1.json" \
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-text "The sun sets behind the mountains, painting the sky in shades of pink and orange.|오늘 아침에 공원을 산책했는데, 새소리와 바람 소리가 너무 기분 좋았어요." \
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-lang "en,ko"
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```
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This will:
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- Generate speech for 2 different voice-text-language pairs
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- Use male voice (M1.json) for the first text in English
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- Use female voice (F1.json) for the second text in Korean
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- Process both samples in a single batch
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### Example 3: High Quality Inference
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Increase denoising steps for better quality:
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```bash
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go run example_onnx.go helper.go \
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-total-step 10 \
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-voice-style "assets/voice_styles/M1.json" \
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-text "Increasing the number of denoising steps improves the output's fidelity and overall quality."
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```
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This will:
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- Use 10 denoising steps instead of the default 5
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- Produce higher quality output at the cost of slower inference
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### Example 4: Long-Form Inference
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The system automatically chunks long texts into manageable segments, synthesizes each segment separately, and concatenates them with natural pauses (0.3 seconds by default) into a single audio file. This happens by default when you don't use the `--batch` flag:
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```bash
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go run example_onnx.go helper.go \
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-voice-style "assets/voice_styles/M1.json" \
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-text "This is a very long text that will be automatically split into multiple chunks. The system will process each chunk separately and then concatenate them together with natural pauses between segments. This ensures that even very long texts can be processed efficiently while maintaining natural speech flow and avoiding memory issues."
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```
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This will:
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- Automatically split the text into chunks based on paragraph and sentence boundaries
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- Synthesize each chunk separately
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- Add 0.3 seconds of silence between chunks for natural pauses
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- Concatenate all chunks into a single audio file
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**Note**: Automatic text chunking is disabled when using `--batch` mode. In batch mode, each text is processed as-is without chunking.
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## Available Arguments
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| Argument | Type | Default | Description |
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|----------|------|---------|-------------|
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| `-use-gpu` | flag | false | Use GPU for inference (default: CPU) |
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| `-onnx-dir` | str | `assets/onnx` | Path to ONNX model directory |
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| `-total-step` | int | 5 | Number of denoising steps (higher = better quality, slower) |
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| `-n-test` | int | 4 | Number of times to generate each sample |
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| `-voice-style` | str | `assets/voice_styles/M1.json` | Voice style file path(s), comma-separated |
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| `-text` | str | (long default text) | Text(s) to synthesize, pipe-separated |
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| `-lang` | str | `en` | Language(s) for synthesis, comma-separated (en, ko, es, pt, fr) |
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| `-save-dir` | str | `results` | Output directory |
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| `--batch` | flag | false | Enable batch mode (multiple text-style pairs, disables automatic chunking) |
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## Notes
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- **Multilingual Support**: Use `-lang` to specify the language for each text. Available: `en` (English), `ko` (Korean), `es` (Spanish), `pt` (Portuguese), `fr` (French)
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- **Batch Processing**: When using `--batch`, the number of `-voice-style`, `-text`, and `-lang` entries must match
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- **Automatic Chunking**: Without `--batch`, long texts are automatically split and concatenated with 0.3s pauses
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- **Quality vs Speed**: Higher `-total-step` values produce better quality but take longer
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- **GPU Support**: GPU mode is not supported yet
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## Building a Binary
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To build a standalone executable:
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```bash
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go build -o tts_example example_onnx.go helper.go
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```
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Then run it:
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```bash
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./tts_example -voice-style "../assets/voice_styles/M1.json" -text "Hello world"
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```
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